
Energy-hungry and efficiency-limited image retrieval: Traditional communication manners generally send all the captured images to the users, while many of the images are not interesting/needed by the users. Therefore, such inefficient communication manners may cause large waste of communication resources of UAVs and consume unnecessary UAV energy. A straightforward solution is to use keyword subscriptions, namely, UAVs push images of interest to users by extracting scene graphs of sensing images that match the subscription words provided by users. However, this solution cannot execute well in the traditional communication manners under the scenarios of limited UAV energy or poor wireless channel. It is because that image packet drop and re-transmission bring large delay. Thus the inefficiency of wireless transmission makes it difficult to realize real-time push according to keyword subscriptions. To this end, it is urgently necessary to design an efficient and energy-efficient semantic-based real-time subscription method to improve subscription accuracy and wireless resource utilization.

Semantic encoding without personality: Existing work ignores the personalized needs of semantic communication for encoding retrieval results and does not set user preferences for the importance of semantically encoded values. In the face of large-scale semantic communication, due to the limited bandwidth resources between UAVs and users, semantic coding is prone to signal fading during transmission, resulting in the drop of important coding values (i.e., important information for users). Therefore, we need to design a personalized semantic coding value weight setting scheme, and design different resource allocation schemes for users with different interests to ensure that important information about user preferences can be efficiently conveyed.

Without insights between wireless fading channel and semantic communication: The semantic triplet drop probability is defined as the probability that the number of error bits in the triplet exceeds the error correction capability. However, existing works rarely consider the drop of encoded information caused by the physical environment during information transmission. A common approach is to use neural networks to model the wireless channel, which cannot help the system design with mathematical analysis [1]}. The problems about the multi-path effect, shadow effect, and co-channel interference for wireless transmission environment affect the communication quality and increase the semantic triplets drop problem during transmission. Thereby, it is essential to study the joint optimization problem of the wireless fading channel and semantic triplet drop probability, and also design a resource optimization scheme.

